Executive Summary

Large organizations and security-focused teams increasingly struggle to answer concrete questions like "which call paths can reach this sensitive API across repos and languages" because modern codebases are larger (many monorepos) and polyglot, and current tools either give noisy pattern matches or require expensive manual code review. This problem affects security engineers, SREs, compliance teams, and engineering managers at companies that ship distributed services or maintain complex dependency graphs. You could build an AI-assisted deterministic static call-chain analyzer: a deterministic whole-program static engine that produces reproducible call and dataflow proofs, plus an LLM-driven query interface that translates natural-language questions into verifiable static queries. The product would target cross-repo analysis, support a prioritized set of language frontends, run incrementally in CI, and export machine-readable proofs for SBOMs and audits—while accepting the hard trade-offs of scaling, language coverage, and handling dynamic language features. The timing is favorable because monorepos and polyglot stacks are expanding, LLMs have primed developers to want natural queries, and regulatory and supply-chain pressures (shift-left security, SBOMs) are creating urgency; the addressable market is roughly 10 million professional developers spending about $2,400/yr on tools, a $24.0B opportunity, with market and revenue scores of 88/100 and 90/100 respectively. Adoption barriers exist—trust in deterministic evidence, integration into existing CI/CD, and the engineering cost of high-quality language frontends—but these are solvable with focused engineering and clear SLAs. To stand out you must combine deterministic, measurable proofs (auditable call-chains, precision/recall SLAs) with an LLM layer strictly limited to query translation and explanation to avoid hallucinations, deliver fast incremental analysis, and provide clear ROI (reduced mean-time-to-remediate, fewer false positives) for initial customers. Competition is medium—there are established SAST and graph tools—so success will require 1–2 killer integrations (code hosts or CI providers), transparent metrics, and a staged language-coverage strategy rather than promising full polyglot support from day one.

Executive Summary

Market Opportunity

AI-assisted deterministic static call-chain analyzer for codebases targets a $24.0B = 10M professional developers x $2,400 avg annual spend on developer tools/security total addressable market with medium saturation and a year-over-year growth rate of 15-25%.

Key trends driving demand: monorepos & polyglot stacks -- larger, interconnected codebases need cross-repo analysis rather than file-level scanning; LLM adoption in dev workflows -- developers want natural queries but require deterministic evidence to act; supply-chain & shift-left security -- compliance and SBOMs increase demand for accurate static call/dataflow analysis.

Key competitors include Semgrep (r2c), Sourcegraph, GitHub CodeQL / GitHub Advanced Security, SonarSource (SonarQube / SonarCloud), Adjacents / Workarounds (Copilot, ChatGPT, clang-tidy, cflow).

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AI-assisted deterministic static call-chain analyzer for codebases

Executive Summary

AI-assisted deterministic static call-chain analyzer for codebases

Executive Summary

Market Validation

Market Opportunity

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